diff --git a/docs/user-guide/advanced/Pandas_API.ipynb b/docs/user-guide/advanced/Pandas_API.ipynb
index 239c4c8..56c8d38 100644
--- a/docs/user-guide/advanced/Pandas_API.ipynb
+++ b/docs/user-guide/advanced/Pandas_API.ipynb
@@ -646,6 +646,110 @@
     "tab.mode(dropna=False)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "f5c66579",
+   "metadata": {},
+   "source": [
+    "### Table.std()\n",
+    "\n",
+    "```\n",
+    "Table.std(axis=0, skipna=True, numeric_only=False, ddof=0)\n",
+    "```\n",
+    "\n",
+    "Return sample standard deviation over requested axis. Normalized by N-1 by default. This can be changed using the ddof argument.\n",
+    "\n",
+    "\n",
+    "**Parameters:**\n",
+    "\n",
+    "| Name         | Type | Description                                                                      | Default |\n",
+    "| :----------: | :--: | :------------------------------------------------------------------------------- | :-----: |\n",
+    "| axis         | int  | The axis to calculate the sum across 0 is columns, 1 is rows.                    | 0       |\n",
+    "| skipna       | bool | not yet implemented                                           | True    |\n",
+    "| numeric_only | bool | Only use columns of the table that are of a numeric data type.                   | False   |\n",
+    "| ddof    | int  | Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. | 1 |\n",
+    "\n",
+    "**Returns:**\n",
+    "\n",
+    "| Type               | Description                                                          |\n",
+    "| :----------------: | :------------------------------------------------------------------- |\n",
+    "| Table         | The std across each row / column with the key corresponding to the row number or column name. |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2767afd",
+   "metadata": {},
+   "source": [
+    "**Examples:**\n",
+    "\n",
+    "Calculate the std across the columns of a table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87b94fd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tab = kx.Table(data=\n",
+    "    {\n",
+    "        'a': [1, 2, 2, 4],\n",
+    "        'b': [1, 2, 6, 7],\n",
+    "        'c': [7, 8, 9, 10],\n",
+    "        'd': [7, 11, 14, 14]\n",
+    "    }\n",
+    ")\n",
+    "tab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e54d557",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tab.std()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14950833",
+   "metadata": {},
+   "source": [
+    "Calculate the std across the rows of a table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f19161ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tab.std(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8ea5a38",
+   "metadata": {},
+   "source": [
+    "Calculate std accross columns with ddof=0:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6361dcb7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tab.std(ddof=0)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7e2813b4",
@@ -1813,6 +1917,136 @@
     "df.astype({'c4':kx.SymbolVector, 'c5':kx.SymbolVector})"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0f8813a0",
+   "metadata": {},
+   "source": [
+    "### Table.add_prefix()\n",
+    "\n",
+    "```\n",
+    "Table.add_prefix(columns)\n",
+    "```\n",
+    "\n",
+    "Rename columns adding a prefix in a table and return the resulting Table object.\n",
+    "\n",
+    "**Parameters:**\n",
+    "\n",
+    "| Name    | Type            | Description                                                         | Default    |\n",
+    "| :-----: | :-------------: | :------------------------------------------------------------------ | :--------: |\n",
+    "| prefix    | str  | The string that will be concatenated with the name of the columns  | _required_ |\n",
+    "| axis    | int                    | Axis to add prefix on.     | 0          |\n",
+    "\n",
+    "**Returns:**\n",
+    "\n",
+    "| Type  | Description                                                        |\n",
+    "| :---: | :----------------------------------------------------------------- |\n",
+    "| Table | A table with the given column(s) renamed adding a prefix.                          |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9186ed86",
+   "metadata": {},
+   "source": [
+    "**Examples:**\n",
+    "\n",
+    "The initial table to which a prefix will be added to its columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f20131b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tab.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73c2b08f",
+   "metadata": {},
+   "source": [
+    "Add \"col_\" to table columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "926c8295",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tab.add_prefix(prefix=\"col_\").head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a4abc8c",
+   "metadata": {},
+   "source": [
+    "### Table.add_suffix()\n",
+    "\n",
+    "```\n",
+    "Table.add_suffix(columns)\n",
+    "```\n",
+    "\n",
+    "Rename columns adding a suffix in a table and return the resulting Table object.\n",
+    "\n",
+    "**Parameters:**\n",
+    "\n",
+    "| Name    | Type            | Description                                                         | Default    |\n",
+    "| :-----: | :-------------: | :------------------------------------------------------------------ | :--------: |\n",
+    "| suffix    | str  | The string that will be concatenated with the name of the columns  | _required_ |\n",
+    "| axis    | int                    | Axis to add suffix on.     | 0          |\n",
+    "\n",
+    "**Returns:**\n",
+    "\n",
+    "| Type  | Description                                                        |\n",
+    "| :---: | :----------------------------------------------------------------- |\n",
+    "| Table | A table with the given column(s) renamed adding a suffix.                          |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c22262b8",
+   "metadata": {},
+   "source": [
+    "**Examples:**\n",
+    "\n",
+    "The initial table to which a suffix will be added to its columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55c1f504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tab.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4687851",
+   "metadata": {},
+   "source": [
+    "Add \"_col\" to table columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e00d0f5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tab.add_suffix(suffix=\"_col\").head()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "718584f8",
@@ -2507,6 +2741,82 @@
     "tab.prod(numeric_only=True)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c87d4f95",
+   "metadata": {},
+   "source": [
+    "### Table.count()\n",
+    "\n",
+    "```\n",
+    "Table.count(axis=0, numeric_only=False)\n",
+    "```\n",
+    "\n",
+    "Returns the count of non null values across the given axis.\n",
+    "\n",
+    "**Parameters:**\n",
+    "\n",
+    "| Name         | Type | Description                                                                      | Default |\n",
+    "| :----------: | :--: | :------------------------------------------------------------------------------- | :-----: |\n",
+    "| axis         | int  | The axis to count elements across 1 is columns, 0 is rows.                | 0       |\n",
+    "| numeric_only | bool | Only use columns of the table that are of a numeric data type.                   | False   |\n",
+    "\n",
+    "**Returns:**\n",
+    "\n",
+    "| Type               | Description                                                          |\n",
+    "| :----------------: | :------------------------------------------------------------------- |\n",
+    "| Dictionary         | A dictionary where the key represent the column name / row number and the values are the result of calling `count` on that column / row. |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6520c195",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tab.count()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce85797d",
+   "metadata": {},
+   "source": [
+    "### Table.skew()\n",
+    "\n",
+    "```\n",
+    "Table.skew(axis=0, skipna=True, numeric_only=False)\n",
+    "```\n",
+    "\n",
+    "Returns the skewness of all values across the given axis.\n",
+    "\n",
+    "**Parameters:**\n",
+    "\n",
+    "| Name         | Type | Description                                                                      | Default |\n",
+    "| :----------: | :--: | :------------------------------------------------------------------------------- | :-----: |\n",
+    "| axis         | int  | The axis to calculate the skewness across 0 is columns, 1 is rows.                | 0       |\n",
+    "| skipna       | bool | Ignore any null values along the axis.                                           | True    |\n",
+    "| numeric_only | bool | Only use columns of the table that are of a numeric data type.                   | False   |\n",
+    "\n",
+    "\n",
+    "**Returns:**\n",
+    "\n",
+    "| Type               | Description                                                          |\n",
+    "| :----------------: | :------------------------------------------------------------------- |\n",
+    "| Dictionary         | A dictionary where the key represent the column name / row number and the values are the result of calling `skew` on that column / row. |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fb5dce1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tab.skew(numeric_only=True)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "499025cb",
diff --git a/src/pykx/pandas_api/pandas_indexing.py b/src/pykx/pandas_api/pandas_indexing.py
index 954896d..e3dfeb0 100644
--- a/src/pykx/pandas_api/pandas_indexing.py
+++ b/src/pykx/pandas_api/pandas_indexing.py
@@ -454,6 +454,32 @@ def rename(self, labels=None, index=None, columns=None, axis=0,
 
         return t
 
+    def add_suffix(self, suffix, axis=0):
+        t = self
+        if axis == 1:
+            t = q('''{[s;t]
+                  c:$[99h~type t;cols value@;cols] t;
+                  (c!`$string[c],\\:string s) xcol t
+                  }''', suffix, t)
+        elif axis == 0:
+            raise ValueError('nyi')
+        else:
+            raise ValueError(f'No axis named {axis}')
+        return t
+
+    def add_prefix(self, prefix, axis=0):
+        t = self
+        if axis == 1:
+            t = q('''{[s;t]
+                  c:$[99h~type t;cols value@;cols] t;
+                  (c!`$string[s],/:string[c]) xcol t
+                  }''', prefix, t)
+        elif axis == 0:
+            raise ValueError('nyi')
+        else:
+            raise ValueError(f'No axis named {axis}')
+        return t
+
     def sample(self, n=None, frac=None, replace=False, weights=None,
                random_state=None, axis=None, ignore_index=False):
         if n is None and frac is None:
diff --git a/src/pykx/pandas_api/pandas_meta.py b/src/pykx/pandas_api/pandas_meta.py
index 39668d5..64a9af0 100644
--- a/src/pykx/pandas_api/pandas_meta.py
+++ b/src/pykx/pandas_api/pandas_meta.py
@@ -154,6 +154,29 @@ def mean(self, axis: int = 0, numeric_only: bool = False):
             tab
         )
 
+    @api_return
+    def std(self, axis: int = 0, ddof: int = 1, numeric_only: bool = False):
+        tab = self
+        if 'Keyed' in str(type(tab)):
+            tab = q.value(tab)
+        if numeric_only:
+            tab = _get_numeric_only_subtable(tab)
+
+        axis_keys = q('{[axis;tab] $[0~axis;cols;`$string til count @] tab}', axis, tab)
+
+        if ddof == len(tab):
+            return q('{x!count[x]#0n}', axis_keys)
+
+        return q(
+            '''{[tab;axis;ddof;axis_keys]
+                tab:$[0~axis;(::);flip] value flip tab;
+                d:$[0~ddof;dev;
+                    1~ddof;sdev;
+                    {[ddof;x] avg sqrt (sum xexp[x-avg x;2]) % count[x]-ddof}ddof];
+                axis_keys!d each tab
+            }''', tab, axis, ddof, axis_keys
+        )
+
     @api_return
     def median(self, axis: int = 0, numeric_only: bool = False):
         tab = self
@@ -245,6 +268,16 @@ def prod(self, axis=0, skipna=True, numeric_only=False, min_count=0):
             min_count
         ), cols)
 
+    @convert_result
+    def skew(self, axis=0, skipna=True, numeric_only=False):
+        res, cols = preparse_computations(self, axis, skipna, numeric_only)
+        return (q(
+            '''{[row]
+                m:{(sum (x - avg x) xexp y) % count x};
+                g1:{[m;x]m:m[x]; m[3] % m[2] xexp 3%2}[m];
+                (g1 each row) * {sqrt[n * n-1] % neg[2] + n:count x} each row
+            }''', res), cols)
+
     @convert_result
     def sum(self, axis=0, skipna=True, numeric_only=False, min_count=0):
         res, cols = preparse_computations(self, axis, skipna, numeric_only)
@@ -311,3 +344,8 @@ def agg(self, func, axis=0, *args, **kwargs): # noqa: C901
             return data
         else:
             return (q('{(flip enlist[`function]!enlist x)!y}', keyname, data))
+
+    @convert_result
+    def count(self, axis=0, numeric_only=False):
+        res, cols = preparse_computations(self, axis, True, numeric_only)
+        return (q('count each', res), cols)
diff --git a/tests/test_pandas_api.py b/tests/test_pandas_api.py
index acfe55f..5bde38e 100644
--- a/tests/test_pandas_api.py
+++ b/tests/test_pandas_api.py
@@ -2029,3 +2029,160 @@ def test_keyed_loc_fixes(q):
         mkt[['k1', 'y']]
     with pytest.raises(KeyError):
         mkt['k1']
+
+
+def test_pandas_count(q):
+    tab = q('([] k1: 0n 2 0n 2 0n ; k2: (`a;`;`b;`;`c))')
+    df = tab.pd()
+
+    qcount = tab.count(axis=1).py()
+    pcount = df.count(axis=1)
+
+    assert int(qcount[0]) == int(pcount[0])
+    assert int(qcount[1]) == 1
+
+    qcount = tab.count().py()
+    pcount = df.count()
+
+    assert int(qcount["k1"]) == int(pcount["k1"])
+    assert int(qcount["k2"]) == 3
+
+    qcount = tab.count(numeric_only=True).py()
+    pcount = df.count(numeric_only=True)
+
+    assert int(qcount["k1"]) == int(pcount["k1"])
+
+
+def test_df_add_prefix(kx, q):
+    t = q('([] til 5; 5?5; 5?1f; (5;5)#100?" ")')
+
+    q_add_prefix = t.add_prefix("col_", axis=1)
+
+    assert(q('~', q_add_prefix, t.pd().add_prefix("col_", axis=1)))
+
+    kt = kx.q('([idx:til 5] til 5; 5?5; 5?1f; (5;5)#100?" ")')
+
+    q_add_prefix = kt.add_prefix("col_", axis=1)
+    assert(q('~', q_add_prefix, kt.pd().add_prefix("col_", axis=1)))
+
+    with pytest.raises(ValueError) as err:
+        t.add_prefix("col_", axis=0)
+        assert 'nyi' in str(err)
+
+    with pytest.raises(ValueError) as err:
+        t.add_prefix("col_", axis=3)
+        assert 'No axis named 3' in str(err)
+
+
+def test_df_add_suffix(kx, q):
+    t = q('([] til 5; 5?5; 5?1f; (5;5)#100?" ")')
+
+    q_add_suffix = t.add_suffix("_col", axis=1)
+
+    assert(q('~', q_add_suffix, t.pd().add_suffix("_col", axis=1)))
+
+    kt = kx.q('([idx:til 5] til 5; 5?5; 5?1f; (5;5)#100?" ")')
+
+    q_add_suffix = kt.add_suffix("_col", axis=1)
+    assert(q('~', q_add_suffix, kt.pd().add_suffix("_col", axis=1)))
+
+    with pytest.raises(ValueError) as err:
+        t.add_suffix("_col", axis=0)
+        assert 'nyi' in str(err)
+
+    with pytest.raises(ValueError) as err:
+        t.add_suffix("_col", axis=3)
+        assert 'No axis named 3' in str(err)
+
+
+def test_pandas_skew(q):
+    tab = q('([] price: 250.0f - 100?500.0f; ints: 100 - 100?200)')
+    df = tab.pd()
+    qskew = tab.skew().py()
+    pskew = df.skew()
+    assert round(float(qskew['price']), 6) == round(float(pskew['price']), 6)
+    assert round(float(qskew['ints']), 6) == round(float(pskew['ints']), 6)
+
+    tab = q('^', q('([]sym:100?`foo`bar`baz`qux)'), tab)
+    df = tab.pd()
+    qskew = tab.skew(numeric_only=True).py()
+    pskew = df.skew(numeric_only=True)
+    assert round(float(qskew['price']), 6) == round(float(pskew['price']), 6)
+    assert round(float(qskew['ints']), 6) == round(float(pskew['ints']), 6)
+
+    tab = q('^', q('([]foo:(5#0n),95?500.0f)'), tab)
+    df = tab.pd()
+    qskew = tab.skew(numeric_only=True, skipna=True).py()
+    pskew = df.skew(numeric_only=True, skipna=True)
+    assert round(float(qskew['foo']), 6) == round(float(pskew['foo']), 6)
+
+    tab = q('_', 5, tab) # discard rows with null "foo"s
+    df = tab.pd()
+    qskew = tab.skew(numeric_only=True, axis=1).py()
+    pskew = df.skew(numeric_only=True, axis=1)
+    print(q('~', qskew, pskew))
+    for r in range(len(qskew)):
+        assert round(float(qskew[r]), 6) == round(float(pskew[r]), 6)
+
+
+def test_std(kx, q):
+    df = pd.DataFrame(
+        {
+            'a': [1, 2, 2, 4],
+            'b': [1, 2, 6, 7],
+            'c': [7, 8, 9, 10],
+            'd': [7, 11, 14, 14]
+        }
+    )
+    tab = kx.toq(df)
+    p_m = df.std()
+    q_m = tab.std()
+    for c in q.key(q_m).py():
+        assert p_m[c] == q_m[c].py()
+    p_m = df.std(axis=1)
+    q_m = tab.std(axis=1)
+    for c in range(len(q.cols(tab))):
+        assert p_m[c] == q_m[q('{`$string x}', c)].py()
+    p_m = df.std(ddof=0)
+    q_m = tab.std(ddof=0)
+    for c in q.key(q_m).py():
+        assert p_m[c] == q_m[c].py()
+
+    p_m = df.std(ddof=4)
+    q_m = tab.std(ddof=4)
+    for c in q.key(q_m).py():
+        assert np.isnan(p_m[c]) == np.isnan(q_m[c].py())
+
+    q['tab'] = kx.toq(df)
+    tab = q('1!`idx xcols update idx: til count tab from tab')
+    p_m = df.std()
+    q_m = tab.std()
+    for c in q.key(q_m).py():
+        assert p_m[c] == q_m[c].py()
+    p_m = df.std(axis=1)
+    q_m = tab.std(axis=1)
+    for c in range(len(q.cols(tab)) - 1):
+        assert p_m[c] == q_m[q('{`$string x}', c)].py()
+
+    df = pd.DataFrame(
+        {
+            'a': [1, 2, 2, 4],
+            'b': [1, 2, 6, 7],
+            'c': [7, 8, 9, 10],
+            'd': ['foo', 'bar', 'baz', 'qux']
+        }
+    )
+    tab = kx.toq(df)
+    p_m = df.std(numeric_only=True)
+    q_m = tab.std(numeric_only=True)
+    for c in q.key(q_m).py():
+        assert p_m[c] == q_m[c].py()
+    p_m = df.std(axis=1, numeric_only=True)
+    q_m = tab.std(axis=1, numeric_only=True)
+    for c in range(len(q.cols(tab))):
+        assert p_m[c] == q_m[q('{`$string x}', c)].py()
+
+    with pytest.raises(kx.QError):
+        q_m = tab.std()
+    with pytest.raises(kx.QError):
+        q_m = tab.std(axis=1)